Lawrence Berkeley National Laboratory

We introduce a novel application of Support Vector Machines (SVMs) to the problem of identifying potential supernovae using photometric and geometric features computed from astronomical imagery. The challenges of this supervised learning application are significant: 1) noisy and corrupt imagery resulting in high levels of feature uncertainty,2) features with heavy-tailed, peaked distributions,3) extremely imbalanced and overlapping positiveand negative data sets, and 4) the need to reach high positive classification rates, i.e. to find all potential supernovae, while reducing the burdensome workload of manually examining false positives. High accuracy is achieved via asign-preserving, shifted log transform applied to features with peaked, heavy-tailed distributions. The imbalanced data problem is handled by oversampling positive examples,selectively sampling misclassified negative examples,and iteratively training multiple SVMs for improved supernovarecognition on unseen test data. We present crossvalidation results and demonstrate the impact on a largescale supernova survey that currently uses the SVM decision value to rank-order 600,000 potential supernovae each night.